Product platform configuration for product families: Module clustering based on product architecture and manufacturing process

Product family design utilizes platform-based modularity to enable product variety and efficient mass-production. While product platform issues have attracted much attention from both academia and industry, traditional product platform design for product families emphasized the platform-based modularity that focuses on product structure dimension (functional or non-functional) to realize cost reductions during the design stage. Both the design architecture and manufacturing process are objectives that define product family modularity (PFM). They should be closely coupled with each other for the planning and configuration of platforms. This paper focuses on the product platform configuration by recognizing and utilizing shared product modules for product families. Instead of clustering product modules only based on their design structure, this approach differentiates each product variant, and considers the inherent relationship between product architecture and processing activities. The advantage is that similar components can be grouped and produced on a shared platform, thus benefitting from lower cost and shorter production time. First, both the architecture and manufacturing information of the product variety are captured in matrix format. Then, hierarchical clustering is applied over the components to generate PFM. Finally, a set of platforms are constructed to efficiently process most components of variants.     

Optimal platform investment for product family design

Existing models for developing modular product families based on a common platform are either too engineering oriented or too marketing centric. In this paper, we propose an intermediate modeling ground that bridges this gap by simultaneously considering essential concepts from engineering and marketing to construct an alternative model for platform-based product families. In this model, each variant (in the platform-based product family) contributes a percentage to overall market coverage inside a target market segment. The extent to which a specific variant contributes to market coverage is linked to its degree of distinctiveness. On the other hand the cost of development of all variants (that constitute the product family) is also dependent on the degree of commonality between these variants. The objective of the model is to maximize market coverage subject to an available development budget. Based on a conceptual design of the product family, the proposed model suggests the optimal initial investment in the platform, the commonality level between variants, and the number of variants to be produced in order to maximize market coverage using both analytical and simulation techniques. An application example using an ice scraper product family is included to demonstrate the proposed model.     

Modular product platform configuration and co-planning of assembly lines using assembly and disassembly

Formation of products platforms is carried out during the planning stage and very often separately from the planning of corresponding assembly lines. There is a dearth of literature which considers the different aspects of fully integrating platform design, product family formation, assembly line design, delayed product differentiation, and new concepts of mass customization. A Modular Product Platform Configuration model which uses assembly and disassembly for configuring product variants and Co-Planning of products platforms (MPCC) and their assembly Lines is presented. It is used to co-plan the common platform components and the associated product families simultaneously with the planning of its corresponding mixed-model assembly line. Using both assembly and disassembly to customize the product family platform in order to generate product variants is not commonly discussed in literature. It is defined as the formation of platforms for use to derive multiple products by including many components not shared by every product. The platform is then customized by assembling or disassembling components to form different product variants. The model is formulated using mixed integer mathematical programming to minimize the number of assembly stations and cycle time. Two case studies are used for verification and demonstration. They illustrated the ability of the MPCC model to integrate the planning of product platform, product families and the number of assembly stations required to assemble and disassemble components from mass-assembled product platforms to derive new product variants.     

Flexible platform component design under uncertainty

Incorporating flexibility into product platforms allows manufacturers to respond to changing market needs with a minimal increase in product family complexity and investment cost. To successfully design a flexible product platform, proper design of flexible platform components is critical. These components can be described as “cousin” parts as they are neither completely unique nor completely common among variants. In this paper, a multidisciplinary process for designing flexible product platform components is introduced, assuming the platform component is decided a priori. The design process starts with identification of uncertainties and generation of multiple design alternatives for embedding flexibility into the component. Design alternatives are then optimized for minimum cost, while satisfying the component performance requirements. The flexible designs are then evaluated for economic profitability under identified uncertainty, using Monte Carlo simulation. At the end, the most profitable flexible component design is selected. The proposed design process is demonstrated through a case study, in which different flexible designs are generated and optimized for an automotive floor pan, an essential element of most vehicle product platforms. Results suggest that the way in which the flexibility is incorporated in the component, production volume trends, and the degree of built-in flexibility are important factors to consider when designing flexible product platforms. D. Chang was retired from General Motors R & D     

An efficient decomposed multiobjective genetic algorithm for solving the joint product platform selection and product family design problem with generalized commonality

Product family optimization involves not only specifying the platform from which the individual product variants will be derived, but also optimizing the platform design and the individual variants. Typically these steps are performed separately, but we propose an efficient decomposed multiobjective genetic algorithm to jointly determine optimal (1) platform selection, (2) platform design, and (3) variant design in product family optimization. The approach addresses limitations of prior restrictive component sharing definitions by introducing a generalized two-dimensional commonality chromosome to enable sharing components among subsets of variants. To solve the resulting high dimensional problem in a single stage efficiently, we exploit the problem structure by decomposing it into a two-level genetic algorithm, where the upper level determines the optimal platform configuration while each lower level optimizes one of the individual variants. The decomposed approach improves scalability of the all-in-one problem dramatically, providing a practical tool for optimizing families with more variants. The proposed approach is demonstrated by optimizing a family of electric motors. Results indicate that (1) decomposition results in improved solutions under comparable computational cost and (2) generalized commonality produces families with increased component sharing under the same level of performance. A preliminary version of this paper was presented at the 2007 AIAA Multidisciplinary Design Optimization Specialists Conference.     

Multiple-platform based product family design for mass customization using a modified genetic algorithm

A successful product family design method should achieve an optimal tradeoff among a set of conflicting objectives, which involves maximizing commonality across the family of products with the prerequisite of satisfying customers’ performance requirements. Optimization based methods are experiencing new found use in product family design to resolve the inherent tradeoff between commonality and distinctiveness that exists within a product family. This paper presents and develops a 2-level chromosome structured genetic algorithm (2LCGA) to simultaneously determine the optimal settings for the product platform and corresponding family of products, by automatically varying the amount of platform commonality within the product family during a single optimization process. The single-stage approach can yield improvements in the overall performance of the product family compared with two-stage approaches, in which the first stage involves determining the best settings for the platform variables and values of unique variables are found for each product in the second stage. The augmented scope of 2LCGA allows multiple platforms to be considered during product family optimization, offering opportunities for superior overall design by more efficacious tradeoffs between commonality and performance. The effectiveness of the proposed approach is demonstrated through the design of a family of universal electric motors and comparison against previous work.     

Product platform design through sensitivity analysis and cluster analysis

Scale-based product platform design consists of platform configuration to decide which variables are shared among which product variants, and selection of the optimal values for platform (shared) and non-platform variables for all product variants. The configuration step plays a vital role in determining two important aspects of a product family: efficiency (cost savings due to commonality) and effectiveness (capability to satisfy performance requirements). Many existing product platform design methods ignore it, assuming a given platform configuration. Most approaches, whether or not they consider the configuration step, are single-platform methods, in which design variables are either shared across all product variants or not shared at all. In multiple-platform design, design variables may be shared among variants in any possible combination of subsets, offering opportunities for superior overall design but presenting a more difficult computational problem. In this work, sensitivity analysis and cluster analysis are used to improve both efficiency and effectiveness of a scale-based product family through multiple-platform product family design. Sensitivity analysis is performed on each design variable to help select candidate platform design variables and to provide guidance for cluster analysis. Cluster analysis, using performance loss due to commonization as the clustering criterion, is employed to determine platform configuration. An illustrative example is used to demonstrate the merits of the proposed method, and the results are compared with existing results from the literature.     

Integrated product and manufacturing system platforms supporting the design of personalized medicines

Pharmaceutical product customization, a prerequisite for personalized medicines, is currently a widely researched topic. Patient characteristics can be mapped and translated into parameters for designing patients’ individual treatment, i.e., the dosage form. However, current pharmaceutical manufacturing is dominated by mass production and lacks the capability and flexibility required to produce customized products. Mass customization is a proven successful approach in, for example, the manufacturing industry and thus has been discussed as an enabler for pharmaceutical product customization but has never been fully explored in a pharmaceutical context. Inspired by mass customization approaches in the manufacturing industry, this study proposes a novel methodology to develop integrated product and manufacturing system platforms for pharmaceutical products supporting a mass customization paradigm. The proposed methodology establishes sets of product and manufacturing system platform variants and suggests an approach to feasible platform design selection. The applicability of the proposed methodology is illustrated for diabetes treatment as a selected case example. Integrated platform designs are developed for the conventional treatment of a fully integral tablet design and for a design enabling product customization with a modularized tablet design. The manufacturing platforms are still embracing a mass production design in the methodology illustration and should elicit knowledge on the utility of the current production design in a mass customization context. The performance and utility of the respective platform are assessed in terms of production cost and patient benefit. The results suggest a substantial increase in patient benefit afforded by the modularized tablet design, however the production cost is increased. This trade-off between the production cost and patient benefit thus calls for novel manufacturing system concepts to achieve the feasible manufacturing of customized pharmaceutical products.     

A cost-driven process planning method for hybrid additive–subtractive remanufacturing

Hybrid manufacturing combines additive manufacturing’s advantages of building complex geometries and subtractive manufacturing’s benefits of dimensional precision and surface quality. This technology shows great potential to support repairing and remanufacturing processes. Hybrid manufacturing is used to repair end-of-life parts or remanufacture them to new features and functionalities. However, process planning for hybrid remanufacturing is still a challenging research topic. This is because current methods require extensive human intervention for feature recognition and knowledge interpretation, and the quality of the derived process plans are hard to quantify. To fill this gap, a cost-driven process planning method for hybrid additive–subtractive remanufacturing is proposed in this paper. An automated additive–subtractive feature extraction method is developed and the process planning task is formulated into a cost-minimization optimization problem to guarantee a high-quality solution. Specifically, an implicit level-set function-based feature extraction method is proposed. Precedence constraints and cost models are also formulated to construct the hybrid process planning task as a mixed-integer programming model. Numerical examples demonstrate the efficacy of the proposed method.     

Extension of the target cascading formulation to the design of product families

The target cascading methodology for optimal product development is extended to product families with predefined platforms. The single-product formulation is modified to accommodate the presence of shared systems, subsystems, and/or components and locally introduced targets. Hierarchical optimization problems associated with each product variant are combined to formulate the product family multicriteria design problem, and common subproblems are identified based on the shared elements (i.e. the platform). The solution of the overall design problem is coordinated so that the shared elements are consistent with the performance and behaviour of the product variants. A simple automotive design example is used to demonstrate the proposed methodology.     

Product platform design to improve commonality in custom products

Many companies find it difficult to maintain commonality and economies of scale in products with strict customer design requirements that may vary greatly from contract-to-contract or piece-to-piece. These strict and varied requirements typically result in highly customized products that are costly to manufacture, involve small production runs, and require long delivery times. In this paper, we discuss how the strategic incorporation of product platforms into the design process can leverage the design effort of individually customized products. The example involves the design of cross-sections for yokes used to mount valve actuators in the nuclear power industry. Through this example we demonstrate the process of creating a market segmentation grid, choosing a targeted segment, creating a product platform for the yoke cross-section, and subsequently defining the yoke product family using the product platform concept exploration method. The end result is a platform-based product family that will improve response to customer requests, reduce design and manufacturing costs, and improve time to market for companies that make small production runs of highly customized products.     

A taxonomy and decision support for the design and manufacture of types of product families

The realization that designing products in families can and does have significant technological and economic advantages over traditional single product design has motivated increasing interest in recent years in formal design tools and methodologies for product family design. However, currently there is no guidance for designers in the first key strategic decisions of product family design, in particular determining the type of product family to design. Hence, in this paper, first a taxonomy of different types of product families is presented which consists of seven types of product families, categorized based on number of products and time of product introduction. Next a methodology is introduced to support designers in deciding which type of product family is appropriate, based upon early knowledge about the nature of the intended product(s) and their intended market(s). From this information it follows both which manufacturing paradigm and which fundamental design strategies are appropriate for each type of product family. Finally, the proposed methodology is illustrated through a case study examining a family of whitewater kayaks.     

Co-development of product and supplier platform

The platform strategy has been implemented to efficiently manage the increased variety in products and manufacturing systems domains by achieving their effective and rapid re-configuration. Despite the increased development of platforms research, their back-end issues such as the supply chain and supplier selection have received little attention. In this research, a methodology that integrates the product platform synthesis with the selection of suppliers to form a supplier platform is introduced. The formed supplier platform is a collection of suppliers capable of supplying the components/modules of the product platform. The supplier platform remains unchanged for product generations, and non-platform suppliers are added or removed as needed for producing different product variants in different production periods. The presented co-development methodology consists of three phases. First, co-platforming is used to map the product requirements to the supplier’s domain; then an intuitionistic fuzzy TOPSIS method is employed to assign weights to the suppliers according to selected criteria. The suppliers are chosen next and their platform is synthesized. A laptop product family is used to illustrate the developed methodology. The significance of this research is the synthesis of a supplier platform which can be used without change for many product variants and many product generations. Its implementation enables the planning and creation of strategic alliances with the product platform suppliers.     

Design synthesis of machining systems using co-platforming

Modern manufacturing environment is characterized by frequent product design changes in order to satisfy evolving customer requirements. Various strategies are implemented in order to efficiently manage the consequences arising from the product design changes starting from product design to planning and manufacturing. This paper focuses on synthesizing manufacturing system using the co-platforming concept which maps product platform features and components to the manufacturing system candidate platform machines. A matrix-based mapping model is proposed in order to determine the candidate platform and non-platform machines. Product-related characteristics including manufacturing features, feature orientation, dimensional and geometrical tolerance, cutting power requirements, workpiece volume and surface finish are considered. Characteristics of machines in the manufacturing system include machining axes, accuracy, working envelop and available power. A case study adopted from an automotive engine cylinder block manufacturer is used for demonstrating synthesizing manufacturing systems, based on co-platforming, which are capable of adapting to new products variants without changes to the platform machines. This prolongs the life of the manufacturing system and reduces costs associated with retooling and replacing it.     

Investigating the commonality attributes for scaling product families using comprehensive product platform planning (CP3)

A product family with a platform paradigm is expected to increase the flexibility of the manufacturing process to market changes, and to take away market share from competitors that develop one product at a time. The recently developed Comprehensive Product Platform Planning (CP³) method (i) presents a generalized model, (ii) allows the formation of product sub-families, and (iii) provides simultaneous identification and quantification of platform/scaling design variables. The CP³ model is founded on a generalized commonality matrix that represents the platform planning process as a mixed-binary nonlinear programming (MBNLP) problem. This MBNLP problem is high-dimensional and multimodal, owing to the commonality constraint. In this paper, the complex MBNLP model is reduced to a tractable MINLP problem without resorting to limiting approximations; along the reduction process, redundancies in the original commonality matrix are also favorably addressed. To promote a better understanding of the importance of a reduced MINLP, this paper also provides a uniquely comprehensive formulation of the number of possible platform combinations (or commonality combinations). In addition, a new commonality index (CI) is developed to simultaneously account for the inter-product commonalities (based on design variable sharing) and the overlap between groups of products sharing different platform variables. To maximize the performance of the product family and the commonality index yielded by the new CP³ model, we apply an advanced mixed-discrete Particle Swarm Optimization algorithm. The potential of the new CP³ framework is illustrated through its application to design scalable families of electric motors. Maximizing the new CI produced families with more commonality among similar sets of motor variants (compared to maximizing the conventional CI), which can be a beneficial platform attribute for a wide range of product families.     

Preliminary design and manufacturing planning integration using web-based intelligent agents

Software agents have been increasingly used in the product and process development in industry over the past years due to the rapid evolvement of the Internet technology. This paper describes agents for the integration of conceptual design and process planning. Agents provide mechanisms to interact with each other. This mechanism is important since both of those processes involve negotiations for optimization. A set of design and planning software agents has been developed. These agents are used in a computer-based collaborative environment, called a multi-agent platform. The main purpose of developing such a platform is to support product preliminary design, optimize product form and structure, and reduce the manufacturing cost in the early design stage. The agents on the platform have access to a knowledge base that contains design and planning rules. These rules are derived from an analysis of design factors that influence process and resource planning, such as product material, form, shape complexity, features, dimension, tolerance, surface condition, production volume, and production rate. These rules are used by process planning agents to provide process planners with information regarding selecting preliminary manufacturing processes, determining manufacturing resources, and constructing feedback information to product designers. Additionally, the agents communicate with WEB servers, and they are accessible by users through Internet browsers. During performing design and planning tasks, agents access the data pertinent to design and manufacturing processes by the programming interfaces of existing computer-aided design (CAD) and manufacturing system. The agents are supported by a developed prototype agent platform. The agents and the platform enable the information exchange among agents, based on a previously developed integrated design and manufacturing process object model.     

Mass production of tooling product families via modular feature-based design to manufacturing collaboration in PLM

Product Lifecycle Management (PLM) is recognized as one of the most effective approaches for better, fast and cheaper product development and management. Mass customization is one of the key technologies in PLM to provide tailored product to end customers with the cost of mass production. Characterized by short production lead-time and dynamic customer requirements with low batch size and high variety, tooling product families are facing the challenge of high production cost. New technology with modular feature-based design to manufacturing collaboration to satisfy the needs of mass production with low cost in tooling industry is developed in this study. A corresponding prototype system is demonstrated to show the efficiency of the technology developed.     

Progressive sharing of modules among product variants

Recent market transition from mass production to mass customization forces manufacturers to design products that meet individual requirements. In order to address the high cost of this practice, manufacturers develop product families with a common platform, whose variants are designed to meet different customer demands. Parallel to this transition, the dynamics of the market forces designers to develop products composed of modules that are standardized as much as possible across products, thus can be more resilient than complete designs in a changing world.Starting from an original set of different components, our method designs a modular common platform and additional modules, shared by subsets of the designs, from which variants are composed.We applied the method to the layout design of a set of products. Consequently, the geometric aspect of the product family optimization is emphasized, but functional aspects related to the product features and to customer needs are also addressed due to their manifestation in the layout. The design search space is explored using shape grammar rules that alter component geometry and therefore, functionality. The search for optimal design is performed using simulated annealing. Given different objective formulations or parameter settings, the method can be used to explore the solution space. A simple example problem demonstrates the feasibility of the method.